Evaluating learning approaches for product assembly: using chunking of instructions, spatial augmented reality and display based work instructions

Augmented Reality (AR) as an assistive technology is a promising tool for novice operators to learn assembly processes. This experiment compared an AR instruction method to display based electronic working instructions (EWI) for product assembly, to assess learning during the first repetitions of the products. In addition, two types of work instructions were used, i.e., standard and chunk instructions. In this experiment a chunk instruction consists of six assembly steps. Effects of the instruction method and type on the learning phase were evaluated with 24 novice operators building two products i.e. Operators were then asked to build the same products without instructions in order to assess learned skills and establish effects on the recall phase, also as a result of instruction method and type. Task completion time (TCT), product quality, operator workload and learning curve were measured. The learning curve, as indicated by the TCT, took place during the first three repetitions of product assembly. Instruction method and instruction type had no effect on the learning curve. Product quality was high and no differences were found between learning conditions. Operator workload revealed that chunking of the instruction increased workload during the learning phase. During the recall phase, the AR group's TCT increased 19.2%, but only for the first product's repetition without instruction. Product quality remained the same during the recall phase, however operator workload was reduced for chunk learned products. This study indicates that chunking of instructions should be avoided for novice workers. Both EWI and AR can be used for teaching new assembly procedures. While AR and EWI are useful during the learning phase, there are indications that these methods might hinder the operator once they required the necessary skills and knowledge to assemble the product. A possible solution is making instructions more adaptive to fit the skill proficiency of the operator.